How to Build a RAG System from Scratch with LangChain

Why should AI engineers build RAG instead of fine-tuning?

RAG lets you inject private, large, or frequently changing knowledge into an LLM at query time without retraining. For a startup shipping fast, that's decisive: you can update the knowledge base by re-injecting documents instead of running an expensive fine-tune. RAG also keeps answers auditable — every response traces back to retrieved chunks, which matters when stakeholders ask 'where did this number come from?' Fine-tuning changes behavior and style; RAG delivers facts. For document Q&A over internal data, RAG wins on cost, speed to update, and traceability.

How do you structure the two RAG pipelines?

RAG always has exactly two steps, and separating them cleanly is the mark of a maintainable system. The Data Injection pipeline loads raw documents with the right loader (e.g. PyMuPDF for PDFs), chunks them with a Recursive Character Text Splitter at chunk_size 1000 and chunk_overlap 100, embeds each chunk, and stores the vectors in a collection. The Retrieval pipeline embeds the user query, runs cosine similarity search, retrieves top-k chunks, injects them into the prompt, and generates the answer.

The non-negotiable rule: both pipelines must use the same embedding model. Mixing models puts vectors in different namespaces and silently breaks similarity search. Encode this as a shared config constant so injection and retrieval can never drift.

How do you compose the RAG chain in LangChain?

Convert your vector store into a retriever with `.as_retriever()` — chains require runnables, not raw stores. Then compose:

1. `RunnablePassthrough` passes the question through unchanged.

2. The retriever fetches relevant chunks.

3. A `format_documents` function strips metadata, passing only `page_content` — this cuts token cost and sharpens the LLM's focus.

4. A prompt template with `{question}` and `{context}` injects both, plus guardrails like 'if you don't know, say so' and 'use three sentences maximum'.

5. The LLM generates.

6. An output parser returns clean text.

Call `chain.invoke(question)` and the whole flow — embed, search, retrieve, format, prompt, generate — runs automatically.

How do you avoid the common production failures?

The pitfalls that break RAG in production are predictable. Never pass the entire document to the LLM — it blows the context window, invites hallucination, and burns budget. Never set chunk_overlap to zero, or you'll sever context at boundaries. Always verify the collection is populated and the collection name matches across both pipelines — a typo means the retriever searches an empty store and returns nothing. Get the vector size right by embedding a test string and reading its dimension before creating the collection with cosine distance.

Remember: the LLM is only as good as the retrieved chunks. Most quality problems are retrieval problems. If answers are weak, inspect what the retriever returned before touching the generation prompt.

What should you build next?

Start with a single PDF and an in-memory or local Qdrant store to validate the loop end to end. Once retrieval quality is solid, extend to multiple documents in one collection, add source metadata for traceability (strip it before context), and tune top-k and chunk_size for your domain. Then wrap the chain behind an API endpoint.

Next step: Spin up a local Qdrant via Docker, load one document, and build the seven-step pipeline — confirm `chain.invoke()` returns a grounded answer before scaling.